Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications

Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms

Every day, people around the world upload 1.2 million videos to YouTube or more than 100 hours per minute, and this number is increasing. The condition of this continuous data will be useless if not utilized again. To dig up information on large-scale data, a technique called data mining can be a solution. One of the techniques in data mining is classification. For most YouTube users, when searching for video titles do not match the desired video category. Therefore, this research was conducted to classify YouTube data based on its search text. This article focuses on comparing three algorithms for the classification of YouTube data into the Kesenian and Sains category. Data collection in this study uses scraping techniques taken from the YouTube website in the form of links, titles, descriptions, and searches. The method used in this research is an experimental method by conducting data collection, data processing, proposed models, testing, and evaluating models. The models applied are Random Forest, SVM, Naive Bayes. The results showed that the accuracy rate of the random forest model was better by 0.004%, with the label encoder not being applied to the target class, and the label encoder had no effect on the accuracy of the classification models. The most appropriate model for YouTube data classification from data taken in this study is Naïve Bayes, with an accuracy rate of 88% and an average precision of 90%

Campus inteligente en la Universidad Militar Nueva Granada: creación de un mapa base y aplicaciones para el monitoreo de árboles en el campus

The GIS smart campuses have been constituted as an efficient system that allows the integration of information from different agencies inside universities, with the use of geographic applications developed for different types of users. This research describes general considerations to begin implementation of a smart GIS at the Nueva Granada Campus, in Cajicá. This phase was developed based on the generation of a campus base map, which is used as a spatial reference for the elaboration of all applications that require associated geographic information, as well as the implementation of a Gisweb system for the monitoring, verification, and updating of the campus trees. The result of the work provides the basis for subsequent systems and applications of location, mobility and efficacy management related with the academic and administrative activities in the university campus. Los campus inteligentes GIS se han constituido como sistemas eficientes que permiten la integración de información de diferentes agencias al interior de las universidades con el uso de aplicaciones geográficas desarrolladas para distintos tipos de usuarios. Esta investigación describe las consideraciones generales para emprender la implementación de un GIS inteligente en el Campus de la Universidad Nueva Granada en Cajicá. Esta fase de desarrollo se fundamentó en la generación de un mapa base del campus, el cual es usado como referencia espacial para la elaboración de todas las aplicaciones que requieren información geográfica asociada, así como la implementación de un sistema Gisweb para el monitoreo, verificación y actualización de los árboles del campus. El resultado del trabajo brinda las bases para sistemas y aplicaciones subsiguientes para la ubicación, movilidad y administración de la eficiencia relacionada con las actividades administrativas y académicas en el campus universitario

Classification of the Fluency Multipurpose of Bank Mandiri Credit Payments Based on Debtor Preferences Using Naive Bayes and Neural Network Method

One that has an important role in generating bank profits is providing credit to customers, but credit also carries a very high risk. For this reason, in providing credit to debtors, of course the bank will utilize the personal data of prospective debtors in detail to avoid the risk of problems that will arise in the future. One of the appropriate risks for banks in providing credit is the behavior of customers who do not pay installments at the time which causes bad loans. To overcome and overcome the many bad events, there is an algorithmic calculation method with an intelligent computing system that helps banks in selecting prospective debtors who will be given credit. There are many algorithmic methods that can be used in this kind of research. This study analyzes the classification of staffing credit based on the criteria that become the Bank's standard.The data used by the author in this study uses existing debtor credit data from 2017 to 2020, the modeling process is carried out using split validation with the Naive Bayes algorithm and Neural Network, with this algorithm the 1,314 datasets is divided into 2 parts, namely 80% used as training data and 20% used as testing data. The results showed that the Neural Network algorithm has better results with a correct value of 84.13%, while the Naive Bayes algorithm only produces a value of 72.62

Agile Processes in Software Engineering and Extreme Programming – Workshops

This open access book constitutes papers from the 5 research workshops, the poster presentations, as well as two panel discussions which were presented at XP 2021, the 22nd International Conference on Agile Software Development, which was held online during June 14-18, 2021. XP is the premier agile software development conference combining research and practice. It is a unique forum where agile researchers, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. XP conferences provide an informal environment to learn and trigger discussions and welcome both people new to agile and seasoned agile practitioners. The 18 papers included in this volume were carefully reviewed and selected from overall 37 submissions. They stem from the following workshops: 3rd International Workshop on Agile Transformation 9th International Workshop on Large-Scale Agile Development 1st International Workshop on Agile Sustainability 4th International Workshop on Software-Intensive Business 2nd International Workshop on Agility with Microservices Programmin

TEKNOLOGI MACHINE LEARNING PADA SISTEM PENDUKUNG PENELITIAN RSYS (RESEARCH SUPPORT SYSTEM)

Students who are still unfamiliar with the revising process focus on the local level, such as grammar, spelling, punctuation, and sentence level. Meanwhile, students who are experts focus on the global level, such as focusing on improving writing goals, ideas, and meanings. When making revisions, students become too focused on the local level rather than the global level. Comments for the local level are ineffective as a guide in the revision process. Therefore, this study aims to build machine learning-based software with the ANN method to classify global or local comments. This study uses a design research methodology with the SDLC model of prototyping. The results show that the RSYS software was successfully built with machine learning accuracy in classifying 19 comments, obtained from 2 documents, with a 95: 0.5 ratio, 94.74%. Whereas in alpha testing, it was stated that the functionality of the RSYS system and machine learning was considered to be functioning correctly. For beta testing, the largest percentage was 85% for ease of operation and convenience in using the application, 75% for website display and navigation availability

A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions

Online portals provide an enormous amount of news articles every day. Over the years, numerous studies have concluded that news events have a significant impact on forecasting and interpreting the movement of stock prices. The creation of a framework for storing news-articles and collecting information for specific domains is an important and untested problem for the Indian stock market. When online news portals produce financial news articles about many subjects simultaneously, finding news articles that are important to the specific domain is nontrivial. A critical component of the aforementioned system should, therefore, include one module for extracting and storing news articles, and another module for classifying these text documents into a specific domain(s). In the current study, we have performed extensive experiments to classify the financial news articles into the predefined four classes Banking, Non-Banking, Governmental, and Global. The idea of multi-class classification was to extract the Banking news and its most correlated news articles from the pool of financial news articles scraped from various web news portals. The news articles divided into the mentioned classes were imbalanced. Imbalance data is a big difficulty with most classifier learning algorithms. However, as recent works suggest, class imbalances are not in themselves a problem, and degradation in performance is often correlated with certain variables relevant to data distribution, such as the existence in noisy and ambiguous instances in the adjacent class boundaries. A variety of solutions to addressing data imbalances have been proposed recently, over-sampling, down-sampling, and ensemble approach. We have presented the various challenges that occur with data imbalances in multiclass classification and solutions in dealing with these challenges. The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques. From the result, it’s clear that the performance of Random Forest classifier with data balances using the over-sampling technique SMOTE is best in terms of precision, recall, F-1, and accuracy. From the ensemble classifiers, the Balanced Bagging classifier has shown similar results as of the Random Forest classifier with SMOTE. Random forest classifier's accuracy, however, was 100% and it was 99% with the Balanced Bagging classifier

The Evolution of Sociology of Software Architecture

The dialectical interplay of technology and sociological development goes back to the early days of human development, starting with stone tools and fire, and coming through the scientific and industrial revolutions; but it has never been as intense or as rapid as in the modern information age of software development and accelerating knowledge society (Mansell and Wehn, 1988; and Nico, 1994, p. 1602-1604). Software development causes social change, and social challenges demand software solutions. In turn, software solutions demand software application architecture. Software architecture (“SA”) (Fielding and Taylor, 2000) is a process for “defining a structural solution that meets all the technical and operations requirements...” (Microsoft, 2009, Chapter I). In the SA process, there is neither much emphasis on the sociological requirements of all social stakeholders nor on the society in w hich these stakeholders use, operate, group, manage, transact, dispute, and resolve social conflicts. For problems of society demanding sociological as well as software solutions, this study redefines software application architecture as “the process of defining a structured solution that meets all of the sociological , technical, and operational requirements…” This investigation aims to l ay the groundwork for, evolve, and develop an innovative and novel sub-branch of scientific study we name the “Sociology of Software Architecture” (hereinafter referred to as “SSA”). SSA is an interdisciplinary and comparative study integrating, synthesizing, and combining elements of the disciplines of sociology, sociology of technology, history of technology, sociology of knowledge society, epistemology, science methodology (philosophy of science), and software architecture. Sociology and technology have a strong, dynamic, and dialectical relationship and interplay, especially in software development. This thesis investigates and answers important and relevant questions, evolves and develops new scientific knowledge, proposes solutions, demonstrates and validates its benefits, shares its case studies and experiences, and advocates, promotes, and helps the future and further development of this novel method of science